Google Cloud solution architecture: Hybrid Search

1. Executive summary and workload overview

[A brief description of the workload, its business goals, and the high-level solution architecture proposed.]

2. Requirements and current state

2.1. Functional requirements

• Business processes: [Details of the business processes supported]
• Activities and use cases: [Details of the key activities and use cases]

2.2. Non-functional requirements

• Security: [Details of the security requirements including compliance, encryption, access control requirements]
• Reliability: [Details of the reliability requirements including SLA, RTO/RPO, backup, redundancy requirements]
• Cost: [Details of the cost constraints and pricing models]
• Operations: [Details of the operational requirements including monitoring, logging, deployment, maintenance requirements]
• Performance: [Details of the performance requirements including latency, throughput, scaling requirements]
• Sustainability: [Details of the sustainability requirements including carbon footprint, resource optimization requirements]

2.3. Current state

[If applicable, describe the current on-premises or other-cloud architecture.]

• Current infrastructure: [Details of existing setup]
• Pain points and drivers for migration/redesign: [Details of the drivers for migration/redesign]

2.4. Dependencies

• Internal dependencies: [Details of internal dependencies including other workloads and internal services]
• External dependencies: [Details of external dependencies including third-party products and on-premises tools]

3. Technical decomposition of the workload

[Technical decomposition of the workload components, breaking down the application into logical layers: data ingestion & embedding pipeline, relational & vector data store, hybrid query engine & reranker, database abstraction & agentic tooling, and web application UI.]

4. Proposed solution architecture

4.1. Google Cloud products and features mapping

[Identify Google Cloud products and features mapped to the technical components. For each component, justify the selection, note alternatives considered, and describe the pros and cons of the recommended product/feature and alternatives.]

4.2. Architecture diagram

[Architecture diagram in Mermaid format showing the relationships and flows between the components of the architecture.]

%% Example hybrid search topology
graph TD
    User([User Browser]) --> LoadBalancer[Application Load Balancer]
    LoadBalancer --> WebApp[Cloud Run Web App]
    WebApp --> MCPToolbox[MCP Toolbox for Databases]
    MCPToolbox --> AlloyDB[(AlloyDB for PostgreSQL)]

    subgraph AlloyDB for PostgreSQL
        AlloyDB -->|Embedding Generation| GeminiEnterpriseAgentPlatform[Agent Platform text-embedding-005]
        AlloyDB -->|ScaNN Vector Search| ScaNNIndex[ScaNN Index Scan]
        AlloyDB -->|In-Database LLM Reranking| Gemini[ Gemini AI Operators]
    end

4.3. Architecture description

[Detailed description of the architecture. Describe the task flow and data flow between the components of the architecture.]

• Data flow: [Describe the flow of data.]
• Tasks/control flow: [Describe the flow of tasks/control.]

5. Design and configuration recommendations

[Best practices and configuration recommendations for each pillar of the Google Cloud Architecture Framework.]

5.1. Security, privacy, and compliance

• Access control: [E.g., IAM least privilege roles (roles/alloydb.client, roles/aiplatform.user), PostgreSQL database-native CREATE ROLE permissions, search_path isolation]
• Data protection: [E.g., CMEK encryption at rest, mTLS Auth Proxy transit encryption, DLP de-identification templates]
• Network Security: [E.g., Direct VPC Egress, Private Services Access/PSC, Cloud Armor WAF rules, disabled default run.app URLs]

5.2. Reliability

• Redundant deployment: [E.g., AlloyDB multi-zone HA instance, regional Cloud Run deployment]
• Backup and DR: [E.g., AlloyDB continuous microsecond PITR backups, cross-region replication]

5.3. Operational excellence

• Monitoring and logging: [E.g., Cloud Logging structured JSON logs, Cloud Trace, AlloyDB Query Insights and System Insights dashboards]
• Infrastructure as Code (IaC): [E.g., Terraform manifests, gcloud CLI commands]

5.4. Cost optimization

• Sizing and scaling: [E.g., Cloud Run scale-to-zero, Cloud Storage Object Lifecycle Management]
• Pricing models: [E.g., Committed Use Discounts (CUDs) for AlloyDB compute]

5.5. Performance efficiency

• Caching and database indexing: [E.g., ScaNN multi-level tree tuning (num_leaves, num_leaves_to_search), B-tree facet indexes, leaf search streaming (scann.satisfy_limit = 'relaxed_order')]
• Data updates: [E.g., Automated embedding generation, automatic ScaNN index self-maintenance (MODE='AUTO')]

5.6. Sustainability

• [E.g., Serverless compute scaling, in-database query execution and AI function evaluation]

6. Deployment guidance

[Instructions and code for deploying the architecture.]

6.1. Deployment prerequisites

• [Prerequisite 1: E.g., Enabling APIs (alloydb.googleapis.com, aiplatform.googleapis.com, run.googleapis.com)]
• [Prerequisite 2: E.g., Installing SDKs/tools (gcloud, terraform, psql)]
• ...and so on

6.2. Step-by-step deployment instructions

1. [Step 1: E.g., Authenticate with Google Cloud and set project environment]
2. [Step 2: E.g., Initialize and apply Terraform infrastructure manifests]
3. [Step 3: E.g., Deploy containerized application and MCP tools to Cloud Run]

7. Validation plan

[Details of the pre-deployment static dry-run checks and post-deployment runtime verification steps (e.g., ScaNN vector query recall measurement using evaluate_query_recall, endpoint health checks).]

8. References

• E.g., Secure agent interactions with MCP Toolbox for Databases
• E.g., RAG infrastructure for generative AI using Agent Platform and AlloyDB for PostgreSQL
• E.g., Best practices for tuning ScaNN index in AlloyDB
• E.g., Choose a connectivity option for AlloyDB
• E.g., AlloyDB security best practices